study and development of an innovative 3g/4g wireless...

Study and development of an innovative

University of TRENTO - Faculty of Engineering

Master of Science in Telecommunications Engineering

Student

Alessandro POLO

Study and development of an innovative

3G/4G wireless network analysis tool

Advisors

Prof. Andrea MASSA Alessandro POLO

TRENTO

31 October 2012

Dr. Giacomo OLIVERI

Ing. Lorenzo GANDINI (Vodafone)

Outline

• Scenario and Goals

• Mobile networks

• Key Performance Indexes• Key Performance Indexes

• Detection Methodologies

• Statistical

• Wavelet Pattern Matching

• Filtering

• Ranking and Classification

2TRENTO, 31 October 2012

• Ranking and Classification

• Filters

• Conclusions and Future Work

• EVoKE

Scenario2G, 3G, 4G Wireless Networks

Vodafone World

238.000 Base Station Sites

One Trillion minutes of calls

216 Petabyes of data

• Heterogeneous (many vendors, technologies)

• Growing (services, users)

Cellular Network

Vodafone Annual Report 2012• Growing (services, users)

• Dynamic (architecture, behavior)

• Basic Component: CELL

• Anomaly Detection

• Anomaly Classification

Challenges

Early 4G

Network

Architecture


How? KPI Analysis

• Anomaly Classification

Key Performance IndexesDiagnostic Stream

• Time Series

• Discrete Multivariate random process

KPI Performance measurement

KPIs

(e.g. number of failed calls: DROPS)

Italy 2G, 3G

> 1 Gb/day to evaluate

• Discrete Multivariate random process

• Mathematical Distribution (PDF)

CELL 122412 (Duomo, MI), Rate of 3G failed connections

Thesis Objectives Anomaly Detection

• Identify anomalies (e.g. peaks/zeros)

• Alarm Ranking

• Minimize False-Alarm Rate

Live DATABASE

KPIs


• Statistical Methods

• Pattern Matching

(Haar Wavelet)

Tools

• Minimize False-Alarm Rate

ApproachKPIs Analysis

CELL 129692 (Medolago, BG), HSDPA Establishment Failure Rate

1. Detection

Live Input: KPIs 1. Detection 2. Filtering Results

RAW data → Detected Alarms → Filtered Alarms →

• Post-process Detected Alarms

1. Detection

• Evaluate RAW data (KPIs)

• Identify any possible anomaly

• Generate Alarms

2. FilteringCELL 142213 (Basaluzzo, AL), Dropped Calls


• Post-process Detected Alarms

• Alarms Aggregation, Ranking

• False-Positive Reduction

• Data size

• Processing TimeConstraints

1. Detectiona) Statistical Approach

Available Voice Traffic Channels of CELL 100868 (Viale Piave, MI)

IDEA Evaluate sample vs. statistics (range, mean, ..) within a Window

IF sample << expected

Example:

Advantages Drawbacks

#Traffic Channels: Low → Alarm

Evaluation Window: Length = N previous samples

IF sample << expected

value (e.g. median)

Anomaly Found

(depends on Detector settings)

Point Anomaly


Advantages

• Simple, Computationally Efficient

• No training

• Universal parameters (thresholds)

Drawbacks

• Window’s Length (lower-bound)

• Data Distribution (PDF), Masking

• Prone to non-zero trend, step

Methods Selection: Which statistical tests?

1. Detectiona) Statistical Methodologies

Method Outlier Test Notes

Fixed and

Adaptive

Threshold

↑ Processing

↓ Robustness, Adaptability

↓ Calibration

thresholdreference

reference>

) -(x i

Threshold ↓ Calibration

Three Sigma

(*Multi-pass)

↑ Processing

↓ Masking

↓ Window Length

MADE3↑ Robustness

↓ MAD Computation

↑ Robustness)~ -(x x

3)~ -(x 0.6745 i >

MAD

x

3ˆ

)ˆ -(x i >σ

x

reference


Robust Estimators

use median instead of mean

Median Rule

↑ Robustness

↑ Performance

↓ PDF Symmetry (skewness)

3.2)~ -(x i >

IQR

x

Universal Thresholds

easier calibration

1. Detection: Examplesa) Statistical Detection

CELL 100942 (Piazza Lima, MI), Dropped Calls

↑ Outlier Detection

Good Results:

CELL 101033 (Rho, MI) , Dropped Calls

↑ Robust to Noise

↑ No Calibration

But still problems:

Collective Anomalies


How to detect patterns?

↓ Prone to Trends

↓ Prone to Steps




Good Results:


↑ No Calibration

But still problems:


↑ Robust to Noise



↓ Prone to Trends

↓ Prone to Steps




↑ Robust to Noise

Good Results:


↑ Robust to Noise

↑ No Calibration

But still problems:




↓ Prone to Trends

↓ Prone to Steps

step

1. Detectionb) Wavelet Comparer

IDEA Pattern Matching in Wavelet Domain

CELL 134801 (Lainate Sud, MI)

Ramp

orWavelets

Why?

CELL 100492 (Basiglio, MI)

or

Trend

Step

Advantages

Which Mother Wavelet?

Wavelets

• Multi-Resolution

• Time and Frequency Localization

Haar Wavelet



Drawbacks

• Based on mean, not robust

• Prone to punctual outliers

• Computational Complexity O(N)

• Easy (local) Trend recognition

1. Detectionb) Wavelet Comparer

Signal = { 9,7,3,5 }

DWT = { 6, 2, 1, -1 }

Haar Decomposition Example

DR

OP

S

CELL 101461 (Meda, MI), Dropped Calls

Does input signal

“match” given pattern?

Input Signal

Approximation2

Details2

DWT = { 6, 2, 1, -1 }

Higher Resolution

Higher Frequency

DR

OP

S

Apply HAAR DWT (Level 2)

Ramp Pattern

Time Domain

Haar Wavelet Domain

(mean)


D2 D11 D1

2Approx2

Details1

Higher Frequency

D11 D1

2

Sign Matching → Trend Matching

Pattern

Found

1. Detectionb) Wavelet Comparer: Example

HS

DP

A E

FR

CELL 101581 (Gattinara Centro, VC), HSDPA Establishment Failure Rate

Monotonic

Step Pattern

HS

DP

A E

FR

Input Signal

Mismatching Sign


D3 D21 D2

2

D11,2,3,4

Approx3Haar DWT

Coefficients →

Pattern

NOT Matching

2. FilteringChallenges

Live Input: KPIs 1. Detection 2. Filtering Results

RAW data → Detected Alarms → Filtered Alarms →

• Aggregation

• Classification

• False-Positives Reduction

Filtering Objectives Alarm Ranking

Network Maintenance Efficiency

Reduce

work load


Network operators are busy,

the system is designed

to improve efficiency

Network Maintenance Efficiency

• Highlight Alarms of Interest

• Automate Best Practices

• Integrate Know-How

How?

2. FilteringIntrinsic Detection Ranking

reference

reference iievent

x- =Score

Alarm Priority? Deviation from Expected value

Is this enough

for domain experts?

High Score!

(e.g. reference = median)

Priority is Affected by

• Previous Behavior

• KPI, Cell relevance

• Not static (change in time)

• ..

CELL 131881 (Arluno Casello A4, MI), Dropped Calls

for domain experts?

No, because:


Lower Score

Smarter Ranking →

• ..

Domain Experts requirebut the Alarm is persistent,

It’s priority↑

2. Filtering

IDEA Tune Score by means of ad-hoc Filters derived by: Domain Experts

Improve Ranking: Example


Persistent Alarm

Filter Filtering


Filter Filtering

Increased Score

2. Filtering

CELL 110907 (Calcinate ,BG) Traffic [Erlangs]Holiday Alarm

• Low Network Load

• Recognize Holidays

Traffic is lower during vacations

IDEA Tune Score by means of ad-hoc Filters derived by: Domain Experts

Lower Score

• Recognize Holidays

• Dynamic Vacation DB

• Reduce Ranking


Persistent Alarm

• Evaluate History

• Time Correlation

• Increase Ranking

Recurring Alarm

Higher Score


• Increase Ranking

• Link Alarms

CELL 100069 (Via Napo Torriani, MI), Dropped Calls

Isolated Alarm

• Future Observations

• Time Correlation

• Reduce Ranking

• Reduce False-Positives

Single Spike, False-Alarm? Lower Score

Summary Results

CELL 113433 (Paderno-castelletto, MI), Dropped Calls

Refined Score = 52

1. Anomalies Detection (green samples)

2. Aggregation, Tagging, Re-Scoring (colored labels)

Refined Score = 87

(even if sample is lower)

Refined Score = 52


↓ False-Positives

Decreased Score

↑ Alarm Notified

Increased Score

with State of the Art

Commercial tool

Comparison →

Less priority More priority

Comparative Assessment (vs. ANTARES)

CELL 113433 (Paderno-castelletto, MI), Dropped Calls, Analysis by Vodafone ANTARES

Vodafone

ANTARES

State of the Art

Commercial tool

-64% False Alarms

45 alarms

Many false

alarms!


EVoKE Demo

Analysis Tool

• Single Day

28 August 2012

• North-West Italy

Network:Network:

2G, 3G Nokia

(31571 cells)

• 4 KPIs 2G (GSM)

• 2 KPIs 3G (HSDPA)

• 4 Detectors

• 5 Filters


16 seconds

Processing Time

EVoKE Geo Viewer

Daily Results

Visual Navigation

Shown Example

• 28 August 2012

• North-West Italy

Network: 3G Nokia

(18087 cells)

• KPIs: 3G HSDPA

• Traffic [Kb]

• Establishment


• Establishment

Failure Rate [%]

Spatial Correlation

NN N Few nearby alarms,

Highest Resolution

Many alarms,

Low resolution

Many alarms,

Higher resolution

Conclusions

Resume

• Scenario: Mobile networks, KPIs

• Detection: Statistical Methods, Haar Wavelet

• Filtering: Ranking and Classification

• Results: EVoKE

Future Work


• Alarms Correlation (Space, Causality)

• Classification (SVM, Bayesian networks)

Thank you


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